from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
		import PIL

# soft dependency
if is_pytesseract_available():
		import pytesseract

__a    :	List[Any]         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				return [
				    int(1000 * (box[0] / width)				),
				    int(1000 * (box[1] / height)				),
				    int(1000 * (box[2] / width)				),
				    int(1000 * (box[3] / height)				),
				]





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase = None				):







				"""simple docstring"""



				__lowercase					     =				tesseract_config if tesseract_config is not None else ''''''

				# apply OCR
				__lowercase					     =				to_pil_image(lowercase				)
				__lowercase     ,	__lowercase					     =				pil_image.size
				__lowercase					     =				pytesseract.image_to_data(lowercase				,     lang=lowercase				,     output_type='''dict'''				,     config=lowercase				)
				__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase					     =				data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

				# filter empty words and corresponding coordinates
				__lowercase					     =				[idx for idx, word in enumerate(lowercase				) if not word.strip()]
				__lowercase					     =				[word for idx, word in enumerate(lowercase				) if idx not in irrelevant_indices]
				__lowercase					     =				[coord for idx, coord in enumerate(lowercase				) if idx not in irrelevant_indices]
				__lowercase					     =				[coord for idx, coord in enumerate(lowercase				) if idx not in irrelevant_indices]
				__lowercase					     =				[coord for idx, coord in enumerate(lowercase				) if idx not in irrelevant_indices]
				__lowercase					     =				[coord for idx, coord in enumerate(lowercase				) if idx not in irrelevant_indices]

				# turn coordinates into (left, top, left+width, top+height) format
				__lowercase					     =				[]
				for x, y, w, h in zip(lowercase				,     lowercase				,     lowercase				,     lowercase				):
								__lowercase					     =				[x, y, x + w, y + h]
								actual_boxes.append(lowercase				)

				# finally, normalize the bounding boxes
				__lowercase					     =				[]
				for box in actual_boxes:
								normalized_boxes.append(normalize_box(lowercase				,     lowercase				,     lowercase				)				)

				assert len(lowercase				) == len(lowercase				), "Not as many words as there are bounding boxes"

				return words, normalized_boxes




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Any     					=		['''pixel_values''']





			def __init__(  self ,  lowerCAmelCase__ = True ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = PILImageResampling.BILINEAR ,  lowerCAmelCase__ = True ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "" ,  **lowerCAmelCase__ ,  )	->   None:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__		)
							__lowercase					     =				size if size is not None else {'''height''': 2_24, '''width''': 2_24}
							__lowercase					     =				get_size_dict(lowerCAmelCase__		)

							__lowercase					     =				do_resize
							__lowercase					     =				size
							__lowercase					     =				resample
							__lowercase					     =				apply_ocr
							__lowercase					     =				ocr_lang
							__lowercase					     =				tesseract_config





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ = PILImageResampling.BILINEAR ,  lowerCAmelCase__ = None ,  **lowerCAmelCase__ ,  )	->   np.ndarray:

							'''simple docstring'''


							__lowercase					     =				get_size_dict(lowerCAmelCase__		)
							if "height" not in size or "width" not in size:
											raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"		)
							__lowercase					     =				(size['''height'''], size['''width'''])
							return resize(lowerCAmelCase__ ,  size=lowerCAmelCase__ ,  resample=lowerCAmelCase__ ,  data_format=lowerCAmelCase__ ,  **lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = ChannelDimension.FIRST ,  **lowerCAmelCase__ ,  )	->   PIL.Image.Image:

							'''simple docstring'''


							__lowercase					     =				do_resize if do_resize is not None else self.do_resize
							__lowercase					     =				size if size is not None else self.size
							__lowercase					     =				get_size_dict(lowerCAmelCase__		)
							__lowercase					     =				resample if resample is not None else self.resample
							__lowercase					     =				apply_ocr if apply_ocr is not None else self.apply_ocr
							__lowercase					     =				ocr_lang if ocr_lang is not None else self.ocr_lang
							__lowercase					     =				tesseract_config if tesseract_config is not None else self.tesseract_config

							__lowercase					     =				make_list_of_images(lowerCAmelCase__		)

							if not valid_images(lowerCAmelCase__		):
											raise ValueError(
											    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
											    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

							if do_resize and size is None:
											raise ValueError('''Size must be specified if do_resize is True.'''		)

							# All transformations expect numpy arrays.
							__lowercase					     =				[to_numpy_array(lowerCAmelCase__		) for image in images]

							if apply_ocr:
											requires_backends(self ,  '''pytesseract'''		)
											__lowercase					     =				[]
											__lowercase					     =				[]
											for image in images:
															__lowercase     ,	__lowercase					     =				apply_tesseract(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
															words_batch.append(lowerCAmelCase__		)
															boxes_batch.append(lowerCAmelCase__		)

							if do_resize:
											__lowercase					     =				[self.resize(image=lowerCAmelCase__ ,  size=lowerCAmelCase__ ,  resample=lowerCAmelCase__		) for image in images]

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							__lowercase					     =				[flip_channel_order(lowerCAmelCase__		) for image in images]
							__lowercase					     =				[to_channel_dimension_format(lowerCAmelCase__ ,  lowerCAmelCase__		) for image in images]

							__lowercase					     =				BatchFeature(data={'''pixel_values''': images} ,  tensor_type=lowerCAmelCase__		)

							if apply_ocr:
											__lowercase					     =				words_batch
											__lowercase					     =				boxes_batch
							return data
import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script




@require_cpu
class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							debug_launcher(test_script.main		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							debug_launcher(test_ops.main		)